Fuzzy Classification System Research Articles

Designing rule-based fuzzy systems for classification in medicine

Decision Support Systems (DSSs) based on Fuzzy Logic are gaining increasing research interest in order to solve classification problems in a wide range of application fields, especially in medicine, where the chance of presenting classification results together with a clear explanation and with a measure of the associated uncertainty is highly appealing. However, designing a fuzzy system is a thorny process, requiring many steps to be accomplished, from the knowledge extraction and representation, to the inference process, until the presentation of results. Therefore, this paper proposes a general procedure for constructing rule-based fuzzy classifiers, according to the system characteristics of performance and interpretability required by the specific application, which can be used with any type of data, and is particularly useful for the medical field requirements. The proposed procedure is based on the naïve Bayes approximation, therefore, the optimization of necessary parameters is performed only once and separately for each variable, thus resulting computationally fast, while later steps of the procedure enable to calculate more complicate models and choose the best one, without any further optimization. Moreover, the choices of all degrees of freedom of the design, associated with the variables constituting the model, their fuzzy partitions, the rule base construction, and the inference process, are suggested in this paper. Some of them are motivated by general considerations regarding systems applied in the medical ambit. Some other design choices depend on the dataset and on the application. In order to provide an objective way for choosing these degrees of freedom, some parameters for defining the required trade-off between performance and interpretability are proposed here. The application of the proposed procedure is guided by showing a running example, using data of the Wisconsin Breast Cancer Dataset. For different values of the trade-off parameters, optimal interpretability, or first-rate performance, or acceptable interpretability and performance are obtained, with respect to the best fuzzy systems applied on the same dataset. Finally, the procedure is applied on a number of benchmark datasets, and outstanding results are achieved in terms of performance, with respect to the best classification methods of the state-of-the-art.

DESIGN OF CAD SYSTEM OF SOLITARY PULMONARY NODULE WITH HARMONY CLASSIFICATION AND FUZZY SYSTEM

Introduction: Lung cancer is the most wide spread from of cancer, with the highest mortality rate worldwide. In this study, a computer-aided detection (CAD) system was developed for lung nodule detection, segmentation and recognition using CT images. So, we use a highly accurate supervised that uses lung images with the aim of assisting physicians in early detection of lung cancer. Methods: First, we segmented the lung area by masking techniques to isolated nodules and determined region of interest. Then, 24 features were extracted from images that included morphological, statistical and histogram. Important features were derived from the images for their posterior analysis with the aid of a harmony search algorithm and fuzzy systems. Results: In order to evaluate the performance of the proposed method, we used the LIDC database. the number of images included a database of 97 images whom 47 were diagnosed with lung cancer. Results of the base method show a sensitivity of 93%. Conclusion: The harmony search algorithm is optimized using fuzzy system for classification. The CAD system provides 93.1% accuracy.

QUALITY ASSESSMENT OF FUZZY CLASSIFICATION: AN APPLICATION TO SOLVENCY ANALYSIS

The aim of this paper is to apply and establish the advantages of using fuzzy classification systems in evaluating the quality of a fuzzy partition. The system is applied in a methodological proposal for the analysis of the business financial solvency in a specific sector. The sectorial analysis of the solvency is considered as an unsupervised fuzzy classification problem and it is sought through the application of the system, to evaluate both the classification and the quality of the established partition, which allows determine whether modify, to eliminate or to add classes to the partition of the sample space.

Realizing Two-View TSK Fuzzy Classification System by Using Collaborative Learning

In this paper, a novel Takagi-Sugeno–Kang (TSK) fuzzy classification system (FCS) is firstly presented for pattern classification tasks. It is distinguished by having the large margin criterion properly integrated into its objective function. In order to exploit the applicability of fuzzy systems in multiview scenarios, the proposed TSK-FCS is extended to a two-view version, called two-view TSK-FCS (TwoV-TSK-FCS), by using a collaborative learning mechanism. The adopted collaborative learning mechanism not only fully considers the independent information of each view, but also effectively discovers the correlation information hidden in the two views. Thus, the performance of TwoV-TSK-FCS can be enhanced accordingly. Comprehensive experiments on two-view synthetic and UCI datasets demonstrate the effectiveness of the proposed two-view FCS.

Offshore wind turbine clutter characteristics and identification in operational C‐band weather radar measurements

The aim of this work is to update ground‐clutter classification methods in weather radar rainfall measurements to more accurately identify clutter pixels from wind farms. Measurements from two dual‐polarised weather radars, based in the United Kingdom, will be used to determine the characteristics of multiple wind farms in the North Sea and the Irish Sea. Currently 21 of the top 25 largest offshore wind farms are located in these regions. The extensive area occupied by the wind farms creates problems for weather radars located in the neighbouring European countries. Datasets of wind‐farm, precipitation and ground‐clutter pixels were aggregated from Thurnham Radar measurements to form novel membership functions that can be used in a fuzzy logic classification system to identify wind‐farm clutter. When only ground‐clutter datasets were used for classification, areas of the radar scans taken up by wind‐farm clutter were misclassified as rainfall. The inclusion of wind‐farm measurements led to an increase in the ability of the algorithm to detect these pixels as clutter, as the Heidke Skill Score increased from 67.4 to 97.8%. However there was a slight increase in the number of precipitation pixels incorrectly classified as clutter, with the false alarm rate increasing from 0.05 to 1.24% when all variables are used. The algorithm performed slightly better when applied to another radar on Hameldon Hill, showing promise for application to the UK network without recalibration of membership functions.

Accuracy-Based Learning Classifier Systems for Multistep Reinforcement Learning: A Fuzzy Logic Approach to Handling Continuous Inputs and Learning Continuous Actions

Despite their proven effectiveness, many Michigan learning classifier systems (LCSs) cannot perform multistep reinforcement learning in continuous spaces. To meet this technical challenge, some LCSs have been designed to learn fuzzy logic rules. They can be largely classified into strength-based and accuracy-based systems. The latter is gaining more research attention in the last decade. However, existing accuracy-based learning systems either address primarily single-step learning problems or require the action space to be discrete. In this paper, a new accuracy-based learning fuzzy classifier system (LFCS) is developed to explicitly handle continuous state input and continuous action output during multistep reinforcement learning. Several technical improvements have been achieved while developing the new learning algorithm. Particularly, we have successfully extended ${Q}$ -learning like credit assignment methods to continuous spaces. To enable direct learning of stochastic strategies for action selection, we have also proposed to use a new fuzzy logic system with stochastic action outputs. Moreover, fine-grained learning of fuzzy rules has been achieved effectively in our algorithm by using a natural gradient learning method. It is the first time that these techniques are utilized substantially in any accuracy-based LFCSs. Meanwhile, in comparison with several recently proposed learning algorithms, our algorithm is shown to perform highly competitively on four benchmark learning problems and a robotics problem. The practical usefulness of our algorithm is also demonstrated by improving the performance of a wireless body area network.

A cloud computing based Big-Bang Big-Crunch fuzzy logic multi classifier system for Soccer video scenes classification

Soccer video summarization and classification is becoming a very important topic due to the world wide importance and popularity of soccer games which drives the need to automatically classify video scenes thus enabling better sport analysis, refereeing, training, advertisement, etc. Machine learning has been applied to the task of sports video classification. However, for some specific image and video problems (like sports video scenes classification), the learning task becomes convoluted and difficult due to the dynamic nature of the video sequence and the associated uncertainties relating to changes in light conditions, background, camera angle, occlusions and indistinguishable scene features, etc. The majority of previous techniques (such as SVM, neural network, decision tree, etc.) applied to sports video classifications did not provide a consummate solution, and such models could not be easily understood by human users; meanwhile, they increased the complexity and time of computation and the associated costs of the involved standalone machines. Hence, there is a need to develop a system which is able to address these drawbacks and handle the high levels of uncertainty in video scenes classification and undertake the heavy video processing securely and efficiently on a cloud computing based instance. Hence, in this paper we present a cloud computing based multi classifier systems which aggregates three classifiers based on neural networks and two fuzzy logic classifiers based on type-1 fuzzy logic and type-2 fuzzy logic classification systems which were optimized by a Big-Bang Big crunch optimization to maximize the system performance. We will present several real world experiments which shows the proposed classification system operating in real-time to produce high classification accuracies for soccer videos which outperforms the standalone classification systems based on neural networks, type-1 and type-2 fuzzy logic systems.

A discriminative subject-specific spatio-spectral filter selection approach for EEG based motor-imagery task classification

Motor-imagery tasks generate event related synchronization and de-synchronization in certain subject-specific frequency ranges of the subject’s ElectroEncephaloGraphy (EEG) signals. The selection of frequency ranges for each subject is important for obtaining better classification accuracy of motor-imagery based Brain Computer Interface (BCI). Further, the spatial filters extracted corresponding to the selected spectral ranges also influence the classification accuracy. In this paper, a subject-specific spatio-spectral filter selection approach using a cognitive fuzzy inference system for classification of the motor-imagery tasks in a two step approach is presented. The cognitive fuzzy inference system (CFIS) employs an evolving interval type-2 system to classify the non-stationary features. The classifier employs a meta-cognitive sequential algorithm to determine both the structure and parameters of the CFIS. In the first step, the CFIS classifier is used to find the desired spectral filters by eliminating those frequency bands that do not affect the classification performance. In the second step, CFIS is used to eliminate those spatial filters which do not affect the performance. The performance of CFIS based spatio-spectral scheme has been evaluated using two publicly available BCI competition data sets and compared with other existing algorithms like FBCSP, DCSP and BSSFO. The results indicate that the proposed approach outperforms the CSP method by approximately 15–18% and other algorithms like FBCSP, DCSP by 8–10%. Compared to a recently proposed algorithm BSSFO, it achieves an improvement of 2%, but is simpler in comparison to BSSFO. The main impact of the work is its ability to handle non-stationarity using interval type-2 sets and provide good classification performance. In general, the proposed CFIS algorithm can be applied in the field of expert and intelligent systems where it is necessary to deal with non-stationary signals.

Modified AHP for Gene Selection and Cancer Classification Using Type-2 Fuzzy Logic

This paper proposes a modification to the analytic hierarchy process (AHP) to select the most informative genes that serve as inputs to an interval type-2 fuzzy logic system (IT2FLS) for cancer classification. Unlike the conventional AHP, the modified AHP allows us to process quantitative factors that are ranking outcomes of individual gene selection methods including t-test, entropy, receiver operating characteristic curve, Wilcoxon test, and signal-to-noise ratio. The IT2FLS is introduced for the classification task due to its great ability for handling nonlinear, noisy, and outlier data, which are common problems in cancer microarray gene expression profiles. An unsupervised learning strategy using the fuzzy c-means clustering is employed to initialize parameters of the IT2FLS. Other classifiers such as multilayer perceptron network, support vector machine, and fuzzy ARTMAP are also implemented for comparisons. Experiments are carried out on three well-known microarray datasets: diffuse large B-cell lymphoma, leukemia cancer, and prostate. Rather than the traditional cross validation, leave-one-out cross-validation strategy is applied for the experiments. Results demonstrate the performance dominance of the IT2FLS against the competing classifiers. More noticeably, the modified AHP improves the classification performance not only of the IT2FLS but of all other classifiers as well. Accordingly, the proposed combination between the modified AHP and IT2FLS is a powerful tool for cancer classification and can be implemented as a real clinical decision support system that is useful for medical practitioners.

Genetic generation of fuzzy systems with rule extraction using formal concept analysis

Fuzzy classification systems have been widely researched with many approaches proposed in the literature. Several methods are available for the automatic definition of fuzzy classification systems, which basically comprehend two tasks: i) the definition of the attributes in terms of fuzzy sets, and ii) the generation of a rule set containing the domain knowledge, named fuzzy rule base. Genetic Fuzzy Systems are used to learn or tune in fuzzy classification systems. Some genetic approaches for learning the fuzzy rule base require the previous extraction of a set of rules to be used as the genetic search space. In this paper, we present the FCA-Based method, a proposal for the automatic generation of fuzzy rule bases, which extracts a set of rules using the formal concept analysis theory directly from data. After extracting the rules forming the genetic search space, FCA-Based uses a genetic algorithm to select the final rule base. The last step of the FCA-Based method is a rule pruning step in order to improve the interpretability of the fuzzy rule bases. The extraction of rules proposed for the FCA-Based algorithm presents polynomial complexity and does not require the predefinition of the number of rules to be extracted. As it extracts rules directly from data, the proposed method avoids the random extraction of rules. It also presents the advantage of automatically extracting rules with variable number of conditions in their antecedents. A feature subset selection method, specifically designed for fuzzy classification systems, is integrated into the FCA-Based method in order to reduce the search space of solutions. The FCA-Based method is detailed and compared to eight different rule-based fuzzy systems. Experimental results using 27 benchmark datasets and a 10-fold cross-validation strategy show that FCA-Based presents higher accuracy and statistically significant difference with seven of the eight compared methods.

Fuzzy-Genetic Model for the Identification of Falls Risk Gait

This paper investigates the effectiveness of a hybrid genetic and fuzzy set algorithm for the recognition of gait patterns in falls risk patients. In a previous work, we have shown the usefulness of fuzzy set techniques for gait pattern identification. In this paper, we apply a genetic algorithm in conjunction with fuzzy logic rules to better select the optimal combination of pathological gait features for improved gait diagnostic capability. Gait features were calculated using minimum foot clearance data collected during continuous walking on a treadmill for 20 older adults. The subjects are composed of two groups, 10 individuals with normal gait, and 10 with a history of falls. Fuzzy rules were extracted from the gait dataset using subtractive clustering. The genetic algorithm was introduced in order to select the optimum combination of gait features. Using cross validation test data, the results indicated that the generalization performance, in terms of accuracy, for the hybrid system was 97.5%, compared to89.3% that was obtained using only the fuzzy system. The generalization performance of the gait classifier was also analyzed by determining the areas under the receiver operating characteristic plot. We observed that an improved gait classification performance became evident when the fuzzy system classifier used a small number of features that were selected by the genetic algorithm.

Applying the Fuzzy Ant-Miner Algorithm to Extract the Success Indicators of Balloon Dilation in PA-IVS

Several studies have sought to identify the parameters that determine the outcome of balloon dilation in pulmonary atresia with ventricular septum. However, none of these studies was based on the ant colony algorithm. In this paper we focus on the implementation of an algorithm based on ant colonies: Fuzzy Ant-Miner. This method uses the concepts of fuzzy logic to extract rules from the training data. These rules are exploited using a Mamdani fuzzy inference system for classification and prediction. The results obtained by this method in the form of fuzzy rules are easy to interpret, and close to human reasoning.

Heterogeneous face matching using geometric edge-texture feature (GETF) and multiple fuzzy-classifier system

A novel and accurate method for matching of heterogeneous faces, such as sketch and near-infrared (NIR) images, with the visible (VIS) photo gallery and vice a versa has been presented here. A new geometric edge-texture feature (GETF) is proposed, which is not only able to capture the edge information but also the texture information. GETF is constructed from the combined information of edge and texture image of same individual. For texture information local binary pattern (LBP) is used, while for edge information canny edge detection is chosen. Edges are sensitive to illumination, so before applying canny edge operation, we convert the image into illumination invariant gradient domain. For each pixel of the edge image, the nearest edge pixel is found. Finally, the total hamming distance between any pixel and its nearest edge pixel of the corresponding texture image gives GETFDist and the angle between them gives the GETFAng feature. To classify the heterogeneous faces we proposed a multiple fuzzy-classifier system, which is a combination of fuzzy partial least square (FPLS) and fuzzy local feature-based discriminant analysis (FLFDA). We have tested statistically that, the proposed classifier performs better than the individual classifiers. In sketch-photo matching, a rank-1 accuracy of 99.66% is achieved in a gallery of 606 photos consisting of CUHK student dataset, AR face dataset, and XM2VTS dataset. In NIR–VIS image matching, a rank-1 accuracy of 99.50% is achieved in a gallery of 400 VIS images from CASIA-HFB dataset.

Applications of type-2 fuzzy logic system: handling the uncertainty associated with candidate-well selection for hydraulic fracturing

The problem of selecting a target formation(s) in a reservoir among a vast number of zones/sub-layers within huge number of hydrocarbon producing wells for hydraulic fracturing (HF) by using interval type-2 fuzzy logic system (IT2-FLS) to maximize their net present value is studied in this paper. Classical fuzzy system which is called type-1 fuzzy logic system is not capable of accurately capturing the linguistic and numerical uncertainties in the terms used and the inconsistency of the expert's decision-making. IT2-FLS is very useful in circumstances where it is difficult to determine an exact membership function for a fuzzy set; hence it is very effective for dealing with uncertainties. In highlighting this need, the question has been answered why IT2-FLS should be used in this study. The procedure of applying this study in the area of HF candidate-well selection is illustrated through a case study in an oil reservoir.

Block and Fuzzy Techniques Based Forensic Tool for Detection and Classification of Image Forgery

In today’s era of advanced technological developments, the threats to the authenticity and integrity of digital images, in a nutshell, the threats to the Image Forensics Research communities have also increased proportionately. This happened as even for the ‘non-expert’ forgers, the availability of image processing tools has become a cakewalk. This image forgery poses a great problem for judicial authorities in any context of trade and commerce. Block matching based image cloning detection system is widely researched over the last 2-3 decades but this was discouraged by higher computational complexity and more time requirement at the algorithm level. Thus, for reducing time need, various dimension reduction techniques have been employed. Since a single technique cannot cope up with all the transformations like addition of noise, blurring, intensity variation, etc. we employ multiple techniques to a single image. In this paper, we have used Fuzzy logic approach for decision making and getting a global response of all the techniques, since their individual outputs depend on various parameters. Experimental results have given enthusiastic elicitations as regards various transformations to the digital image. Hence this paper proposes Fuzzy based cloning detection and classification system. Experimental results have shown that our detection system achieves classification accuracy of 94.12%. Detection accuracy (DAR) while in case of 81×81 sized copied portion the maximum accuracy achieved is 99.17% as regards subjection to transformations like Blurring, Intensity Variation and Gaussian Noise Addition.

Design of Fuzzy Classification System Applying Expertise Based on Genetic Algorithms

In this paper, a design method of applying rational expertise to the learning mechanism based on fuzzy genetic algorithm is proposed. Firstly, we introduce the accuracy and explanatory of the fuzzy classification system, and then the application of expertise to the fuzzy system, and the adjustment of the member function using genetic algorithm to obtain the final classification results. Finally, the classification results of our experiments demonstrate the effectiveness of the design method with the Irvine classification sample data, and we can obtain better accuracy and explanatory through this learning mechanism with the designed fuzzy classification system.

Fusion of hyperspectral and LIDAR data using decision template-based fuzzy multiple classifier system

Regarding to the limitations and benefits of remote sensing sensors, fusion of remote sensing data from multiple sensors such as hyperspectral and LIDAR (light detection and ranging) is effective at land cover classification. Hyperspectral images (HSI) provide a detailed description of the spectral signatures of classes, whereas LIDAR data give height detailed information. However, because of the more complexities and mixed information in LIDAR and HSI, traditional crisp classification methods could not be more efficient. In this situation, fuzzy classifiers could deliver more satisfactory results than crisp classification approaches. Also, referring to the limitation of single classifiers, multiple classifier system (MCS) may exhibit better performance in the field of multi-sensor fusion. This paper presents a fuzzy multiple classifier system for fusions of HSI and LIDAR data based on decision template (DT). After feature extraction and feature selection on each data, all selected features of both data are applied on a cube. Then classifications were performed by fuzzy k-nearest neighbour (FKNN) and fuzzy maximum likelihood (FML) on cube of features. Finally, a fuzzy decision fusion method is utilized to fuse the results of fuzzy classifiers. In order to assess fuzzy MCS proposed method, a crisp MCS based on support vector machine (SVM), KNN and maximum likelihood (ML) as crisp classifiers and naive Bayes (NB) as crisp classifier fusion method is applied on selected cube feature. A co-registered HSI and LIDAR data set from Houston of USA was available to examine the effect of proposed MCSs. Fuzzy MCS on HSI and LIDAR data provide interesting conclusions on the effectiveness and potentialities of the joint use of these two data.

Rules discovery in fuzzy classifier systems with PSO for scheduling in grid computational infrastructures

Particle swarm optimization (PSO) is a bio-inspired optimization strategy founded on the movement of particles within swarms. PSO can be encoded in a few lines in most programming languages, it uses only elementary mathematical operations, and it is not costly as regards memory demand and running time. This paper discusses the application of PSO to rules discovery in fuzzy classifier systems (FCSs) instead of the classical genetic approach and it proposes a new strategy, Knowledge Acquisition with Rules as Particles (KARP). In KARP approach every rule is encoded as a particle that moves in the space in order to cooperate in obtaining high quality rule bases and in this way, improving the knowledge and performance of the FCS. The proposed swarm-based strategy is evaluated in a well-known problem of practical importance nowadays where the integration of fuzzy systems is increasingly emerging due to the inherent uncertainty and dynamism of the environment: scheduling in grid distributed computational infrastructures. Simulation results are compared to those of classical genetic learning for fuzzy classifier systems and the greater accuracy and convergence speed of classifier discovery systems using KARP is shown.

An interval type-2 T-S fuzzy classification system based on PSO and SVM for gender recognition

In this paper, an interval type-2 Takagi-Sugeno fuzzy classification system (IT2T-SFCS) learned by particle swarm optimization (PSO) and support vector machine (SVM) for antecedent and consequent parameters optimization is proposed. The IT2T-SFCS is constructed by fuzzy if-then rules whose antecedents are interval type-2 fuzzy sets and consequents are linear state equations. The antecedents of IT2T-SFCS use the fuzzy iterative self-organizing data analysis technique (ISODATA) and PSO to learn and calculate the optimal centers and the uncertain widths of the Gaussian membership functions. Consequent parameters in IT2T-SFCS are learned through SVM for the purpose of achieving higher generalization ability. The proposed IT2T-SFCS is able to directly handle uncertainties, minimize the effects of uncertainties and get the better generalization performance, which inherits the benefits of interval type-2 T-S fuzzy system and SVM. For demonstration, IT2T-SFCS is used as a classifier in gender recognition. The experimental results show that the performance of the proposed IT2T-SFCS is superior to that of the previous mainstream classifiers.

Multi-task TSK fuzzy system modeling using inter-task correlation information

The classical fuzzy system modeling methods have been typically developed for the single task modeling scene, which is essentially not in accordance with many practical applications where a multi-task problem must be considered for the given modeling task. Although a multi-task problem can be decomposed into many single-task sub-problems, the modeling results indeed tell us that the individual modeling approach will not be very suitable for multi-task problems due to the ignorance of the inter-task latent correlation between different tasks. In order to circumvent this shortcoming, a multi-task Takagi–Sugeno–Kang fuzzy system model is proposed based on the classical L2-norm Takagi–Sugeno–Kang fuzzy system in this paper. The proposed model cannot only take advantage of independent information of each task, but also make use of the inter-task latent correlation information effectively, resulting to obtain better generalization performance for the built fuzzy systems. Experiments on synthetic and real-world datasets demonstrate the applicability and distinctive performance of the proposed multi-task fuzzy system model in multi-task modeling scenarios.